This position paper argues that contemporary AI paradigms are insufficient for supporting complex global goals and introduces Planet-Centered AI (PCAI) as a design philosophy and research agenda that reorients AI toward planetary-scale socio-ecological systems and their long-term trajectories. A planet-centered approach is grounded in systems thinking, treating Earth as an interconnected whole of which humans are part. We diagnose recurring limitations across AI frameworks, many of which remain human-centered, and show why these become especially consequential under current planetary conditions characterized by systemic risk, non-stationarity, and deep uncertainty. We then articulate how PCAI reshapes the AI lifecycle, from problem formulation and model design to evaluation and deployment, by emphasizing alignment with global agendas, developing system-aware AI foundations, trajectory-oriented evaluation, and monitorability. Finally, we advance a falsifiable claim: AI systems optimized without explicit consideration of systemic consequences are more likely to exacerbate systemic instability than to mitigate it.

翻译：本文立场论文认为，当代人工智能范式不足以支持复杂的全球目标，并引入“以地球为中心的人工智能”（Planet-Centered AI, PCAI）作为一种设计理念和研究议程，将人工智能重新导向行星尺度的社会生态系统及其长期发展轨迹。以地球为中心的方法基于系统思维，将地球视为一个包含人类在内的相互关联的整体。我们诊断了人工智能框架中反复出现的局限性，其中许多仍以人类为中心，并论证为何在当前以系统性风险、非平稳性和深度不确定性为特征的星球条件下，这些局限性尤为重要。随后我们阐述PCAI如何通过强调与全球议程对齐、发展系统感知型人工智能基础、面向轨迹的评估以及可监控性，重塑人工智能生命周期，从问题定义、模型设计到评估与部署。最后，我们提出一个可证伪的主张：未明确考虑系统后果而优化的人工智能系统，更可能加剧而非缓解系统不稳定性。